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Read Full Judgement in 2G Spectrum Case: All Accused Acquitted by CBI Court

Former telecom minister A Raja, Rajya Sabha MP Kanimozhi and 15 other accused have been acquitted in the 2G spectrum scam case on Thursday. The verdict was pronounced by the special CBI court in New Delhi. It was delivered on the cases lodged by CBI and Enforcement Directorate against A Raja, Kanimozhi and others.

The trial in 2G spectrum scam, which cost the-then UPA government heavily, began in 2011 after the court had framed charges against 17 accused in the CBI’s case. All the accused in these cases have denied the allegations levelled against them by the CBI and the ED.

In October 2011, the court had framed charges against them under various provisions of the IPC and the Prevention of Corruption Act dealing with offences of criminal conspiracy, cheating, forgery, using as genuine fake documents, abusing official position, criminal misconduct by public servant and taking bribe.

In its chargesheet filed in April 2011 against Raja and others, CBI had alleged that there was a loss of Rs 30,984 crore to the exchequer in allocation of 122 licences for 2G spectrum which were scrapped by the Supreme Court on February 2, 2012.

A Raja, who was the Minister for Communications and IT in the UPA government, was identified by the CBI as the main accused. The agency had alleged that Raja asked for a cut-off date for applications of 2G licences, even though “no such cap” was recommended by TRAI, to favour certain companies. For this, Raja entered into a criminal conspiracy with his private secretary R K Chandolia, Unitech MD Sanjay Chandra and DB Group officials Shahid Balwa and Vinod Goenka. The telecom department proposed October 10, 2007 as the cut-off date, but Raja, first, brought it forward to October 1, and then to September 25, allegedly after Chandolia was informed that Unitech had applied on September 24.

Raja has always maintained that “every decision” taken by him was “defended by the Manmohan Singh-led UPA government”. “The UPA government and the PM continued to support these decisions in Parliament,” Raja’s counsel had said..

The CBI, then, filed a second chargesheet in the case, accusing Essar of using Loop Telecom as a “front” to secure 2G licences in 2008, thereby cheating the telecom department. Apart from Essar and Loop Telecom, other companies named in the chargesheet were Saraf, Loop Mobile India Ltd and Essar Tele Holding Ltd (ETHL).

Later, the Enforcement Directorate (ED) had filed a chargesheet against Raja, DMK supremo Karunanidhi’s daughter Kanimozhi and others for money laundering. The ED had alleged that Rs 200 crore was paid by Swan Telecom Pvt Ltd (STPL) promoters to DMK-run Kalaignar TV and listed 19 accused in its charge sheet in April 2014. Karunanidhi’s wife Dayalu Ammal was also named in the chargesheet.

In her defence, Kanimozhi had argued that she was the director of Kalaignar TV for only two weeks, from June 6 to June 20, 2007, and was not involved in the scandal.

The court decision will certainly have political aspersions. The verdict comes at a time when the DMK is trying to gain a foothold in Tamil Nadu following the death of Jayalalithaa and split in AIADMK.

READ & DOWNLOAD 2G SPECTRUM CASE FULL JUDGEMENT HERE:

[embeddoc url=”http://legaldesire.com/wp-content/uploads/2017/12/367657552-CBI-vs-A-Raja-and-Others-1.pdf” download=”all”]

The 2G SPECTRUM SCAM HISTORY & TIMELINE:

The cancellation of telecom licences by the Supreme Court in 2012 in the 2G case prompted the key stakeholders — the government and telecom operators — to revamp the way the telecom sector functioned in India, especially how airwaves were offered to operators. While on the policy front, spectrum allocation started happening through auctions, for corporates, especially foreign players, the verdict forced many to cut back on their India exposure. Consequently, the verdict impacted the bank books, loans to telecom companies and infrastructure providers, comprising nearly 3% of the portfolios for lenders at that time.

The companies that saw their licences being cancelled included those with investments from foreign players, such as Norway’s Telenor, the UAE’s Etisalat, Russia’s Sistema, Bahrain Telecommunications, Malaysia’s Maxis, etc. While initially the industry expressed concern over the impact on foreign investment in India’s telecom market, analysts suggest that allocating spectrum at lower prices in fact opened the door to players who may not have been serious about rolling out networks at that point in time.

In 2012, from the pool of operators, 18 had their licences cancelled. As of date, 11 companies offer mobile services in the country, and if the proposed mergers and acquisitions go through, only five operators will remain — Bharti Airtel, Vodafone-Idea, Reliance Jio, BSNL and MTNL. This is in line with developed telecom markets where four or five companies dominate the sector.

“Mr Raja’s approach created an artificial demand for spectrum by keeping it under-priced. If the spectrum was priced closer to its real value, it would not attract so many players. Non-serious players would not have sought it,” Mahesh Uppal, Director, ComFirst India, said.

Compared with how spectrum is allocated to companies after auctioning it today, under A Raja, the Department of Telecommunications (DoT) sold licences to operate 2G services along with spectrum deemed necessary to deploy these services. Following the cancellation of licences, Norwegian telecom company Telenor, which offered services in India in a joint venture with real estate firm Unitech, said that it had already invested over Rs 6,100 crore in equity and over Rs 8,000 crore in corporate guarantees, and that the company was “shocked to see that Uninor is being penalised for faults the court has found in government procedure.”

Bundling the spectrum with the licences and selling this on a first-come-first-served basis resulted in the allocation of airwaves at a much lower price than the market would have dictated — Rs 1,76,000 crore — as estimated by the Comptroller and Auditor General (CAG) of India.

However, the case — in which charges against several bureaucrats were also framed — impacted how the auction process was designed to allocate spectrum at market discovered prices. The latest example was seen during the 2016 auction where, despite a clear lack of intent from the industry, the highly priced spectrum in the 700 MHz frequency was put under the hammer. In its internal pre-auction estimates, even the DoT did not expect the 700 MHz spectrum to be fully sold. Throughout the auction, not a single bid by any operator was placed.

“The problem is very simple. If demand exceeds supply, auctions are your best bet. The process of auction itself is not the issue, it is the design. There are many different ways of conducting the auction. For example, if the government said auction winners would have to make the payment within a week instead of 10 years, many companies would exercise caution. Similarly, if there was a penalty if the services aren’t rolled out within, say, two months of allocation, some players would have opted out,” Uppal commented.

“The government also designed the auction with the primary focus of maximising revenues and not to speed up rollout of broadband or improve the quality of services. If you do that, then you increase the harm an auction can do. If instead, for example, the government had sought bids of how much revenues companies would share with it, there would have been less harm to service growth or consumers,” Uppal said.

The government’s pursuit of higher revenues through auctions, which is seen to be a direct result of the CAG’s observations on the loss of revenue through administrative allocation, has also resulted in spectrum becoming costlier, one of the factors cited by telecom players analysing the financial condition of these companies.

Earlier this year, the Reserve Bank of India red-flagged the telecom industry and asked banks to review their exposure to the sector. An inter-ministerial group was formed, headed by DoT Secretary Aruna Sundararajan, to suggest measures for reducing financial stress in the sector.

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What was the 2G spectrum scam? 10 things to know

While the special cbi judge op saini has acquitted all the accused in the 2g scam case, find out 10 things about the fraud that costed the country rs 1.76 lakh crore..

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Large crowds had stalled the court proceedings of the 2G scam earlier today, a fraud that shook the country and was ranked as the world's second-biggest abuse of executive power by the Time magazine. However, after a brief delay the court announced that all accused have been acquitted in the 2G scam case.

"The prosecution has miserably failed to prove its case, and all accused are acquitted," Special CBI judge OP Saini is reported to have said while acquitting all accused in the 2G scam case.

Former Telecom Minister A Raja, along with 14 others, was the prime accused in the 2g scam case, and in the dock are three companies as well, namely Swan Telecom, Reliance Telecommunications and Uninor.

According to the Comptroller and Auditor General of India, the scam has caused a notional loss of Rs 1.76 lakh crore to the Indian national exchequer.

10 things you need to know about the 2G scam in India:

1. What is the 2G scam?

- It is a combination of three cases, one filed by Enforcement Directorate and two cases registered by the CBI.

A report by the CAG of India revealed that 2G, or second generation licenses for mobile networks, were given at throwaway prices instead of carrying free and fair auctions.

The then Telecom minister, A Raja, denied all charges and said that the decisions were made after then-apprising PM Manmohan Singh. A Raja was arrested in 2011 on charges of cheating, forgery and conspiracy.

2. The primary allegation on A Raja was of allocating airwaves and licenses for cellphone networks in exchange for bribes. According to allegations put on DMK MP Kanimozhi, he had played a major role in facilitating the 217-crore bribe from Swan Telecom to Kalaingar TV, the propaganda arm of the DMK party.

3. The CAG report says that all the demand drafts were actually backdated, thereby implying that the telecom operators had prior knowledge of the licences that were to be issued.

4. The CBI charge sheet says Swan telecom was actually a front for Reliance Telecom and that Reliance violated telecom policy to acquire more than 10 per cent share as allowed under the law.

5. In 2008, a PIL against A Raja's alleged wrongdoings was admitted in the Delhi High Court. During the PIL hearing, the court found out about the so-called lies that the telecom ministry provided.

8. The Supreme Court then imposed a fine of Rs 5 crore each on Unitech Wireless, Swan telecom and Tata Teleservices. The court further said A Raja "wanted to favour some companies at the cost of the public exchequer" and "virtually gifted away important national asset".

9. The wide coverage given to the 2G spectrum scam rocked the UPA II government. It is considered to be among chief factors that led to the downfall of Manmohan Singh government in the 2014 Lok Sabha polls.

In 2011, the then Prime Minister Manmohan Singh had also admitted that this government seemed to have "failed in managing perceptions". "It is quite possible that we have failed to manage perceptions. We should focus on changing perceptions", he told journalists.

Later, in 2014, Raja had claimed that certain forces who were angry with him wrote letters to Manmohan Singh but "he was unaware of the telecom policy."

10. After A Raja resigned, Kapil Sibal took charge of the telecom ministry and came up with the "zero loss" theory in 2011. He claimed that no or zero loss was caused by distributing 2G licenses on first-come-first-served basis.

FOR LATEST UPDATES ON 2G SCAM : 2G scam verdict LIVE: Huge relief for A Raja ; all accused in case acquitted in single-line judgment Published By: AtMigration Published On: Dec 21, 2017 --- ENDS ---

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The great 2G scam: What it was, and what changed after that

A special court on thursday acquitted all accused in the 2g spectrum allocation case as the “prosecution miserably failed to prove any of the charges”. we take a look back at the 2g scam which has failed to be proven, what the entire controversy was, and what changed after that..

2g scam verdict, 2 g scam case, 2g spectrum case verdict, a raja, kanimojhi, manmohan singh, 2g scam case verdict, narendra modi, subramanian swamy, p chidambaram, kapil sibal

(Update: The Central Bureau of Investigation has moved Delhi High Court against the acquittal of ex-telecom minister A Raja, DMK MP Kanimozhi, other accused in the 2G spectrum case)

The great 2G scam

Loss to the nation: Rs 17,66,45,00,00,000 . This is what the Comptroller and Auditor General (CAG) report showed when India “gifted away an important national asset at throwaway prices”, instead of allocating the 2G spectrum to the highest bidder in 2008. The calculated loss stood at Rs 1.77 lakh crore. However, a special court on Thursday acquitted all accused as the “prosecution miserably failed to prove any of the charges”. We take a look back at the 2G scam which has failed to be proven, what the entire controversy was, and what changed after that.

What is spectrum?

Spectrum is a collection of electromagnetic waves that allows transmission sound and data. Spectrum is a natural resource and a national asset. The government allocates frequencies of these electromagnetic waves to telecom and communication companies to operate their voice and data services.

semiconductor, semiconductor fabrication, industry

Spectrum allocation auction — the one that did not happen in 2008

India is one the first countries to adopt the allocation of spectrum through auction or bidding, going as back as 1994. It first happened in 1994 when the government of India auctioned radio waves in 900 MHz band. It was done again in 1995, 1997, 2000. The government auctioned radio spectrum in the 1800 MHz band using a three-stage auction process in 2001. Later, the auction took place in 2010, 2012, 2013, 2014, 2015, 2016 and 2017.

Interestingly, the only time the auction of the spectrum did not take place was in 2008, the year in question now. At the time, the 2G spectrum was allocated to operators for nominal fees.

The 2G scam

On 17 December 2011, then Janata Party leader Subramanian Swamy stood in the witness box and read a letter written by then Finance Minister P Chidambaram to then telecom minister A Raja, which the latter is accused of ignoring. P Chidambaram in the letter had said: Spectrum is a scarce resource. The price for the spectrum should be based on its scarcity value and efficiency of usage. The most transparent method of allocating spectrum would be through auction.

But, it did not happen this way. The 2008 2G spectrum allocation was done using the first-come-first-serve basis and the price was fixed at the market price discovered way back in 2001. The CAG said it led to a loss of Rs 1.77 lakh crore in the state coffers. But there is more to the controversy. Besides the allegation of allocating the spectrum at “throwaway prices”, it was also alleged that licenses were given to a handful of arbitrarily selected companies with no previous telecom experience; the cut-off date for applying was arbitrarily announced which made many companies lose out; and TRAI recommendations were cherry-picked to favour few select players.

Post 2G controversy

In 2012, the Supreme Court of India cancelled 122 licences given out in 2008, calling the allocation unfair. After the 2G controversy, the government switched back to the auctioning of the spectrum, and indeed witnessed highly competitive bidding, lending credence to the high revenue loss figures expounded by the then CAG Vinod Rai.

In 2010, 3G and 4G telecom spectrum were auctioned and the government received a total revenue of Rs 1.06 lakh crore, demonstrating the price that this resource could actually command in the market. Ever since the governments under the UPA-II and the NDA have allocated spectrums using competitive bidding and received huge sums of money as revenue.

Now, further emphasising on the value of spectrum, the present telecom minister Manoj Sinha said after the court verdict on Thursday that the government raised Rs 1.9 lakh crore in 2015 and Rs 65,789 crore in 2016, which was much more than that under the first-come-first-serve method.

First published on December 21, 2017, on http://www.financialexpress.com when the special court in Delhi acquitted all accused in the 2G case.

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All acquitted in 2G spectrum case: A timeline of the events

The court has acquitted all the accused involved in the 2g scam case..

Mumbai: The Court has acquitted all the accused involved in the 2G scam case . “The Court said that the prosecution has miserably failed to prove any of its charges. Thus, all accused are acquitted,” said Vijay Aggarwal, Lawyer of Swan Telecom promoters Shahid Usman Balwa, Vinod Goenka and others.

The verdict for the 2G scam, deemed as one of the biggest scams of independent India, pronounced today by a special CBI court, almost after a six-year trial.

Former telecom minister A Raja, DMK MP Kanimozhi as well as officials of the Reliance Anil Dhirubhai Ambani group, Unitech Ltd and D B Realty were accused in the cases.

The court has been hearing three cases, two filed by the CBI and one by the Enforcement Directorate.

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Following is the chronology of events related to the scam:

December 21, 2017: All accused acquitted, says Supreme Court

December 5, 2017: Special Court announced that it would deliver its verdict on December 21 in the 2G spectrum allocation graft cases

November 7, 2017: The court postpones the decision on fixing a date for the pronouncement of the verdict in the 2G spectrum allocation case. This was the second time the court had postponed its decision.

October 25, 2017: Special Court adjourns the case to November 7.

October 7, 2016: Entitled to be acquitted in 2G case: Kanimozhi

November 3, 2015: Apex court rejects Kanimozhi’s plea to quash charges against her

August 19, 2015: CBI files assets case against Raja

June 2015 : Kalaignar TV got Rs. 200 crores through the 2G scam, says ED

October 31, 2014: Charges framed in 2 G-linked cases

December 9, 2013: JPC report on 2G tabled in Lok Sabha

August 24, 2012: SC dismisses plea for probe against Chidambaram in the 2G case

March 14, 2011: The Delhi High Court sets up special court to deal exclusively with 2G cases

March 29, 2011: Two more persons — Asif Balwa and Rajeev Agarwal arrested

April 25, 2011: CBI files second charge sheet naming DMK chief M. Karunanidhi’s daughter and MP Kanimozhi, 4 others. CBI had alleged that there was a loss of Rs 30,984 crore to the exchequer in allocation of 122 licences for 2G spectrum which were scrapped by the Supreme Court on February 2, 2012.

February 10, 2011 : SC asks CBI to bring under its scanner corporate houses which were beneficiaries of the 2G spectrum.

November 2010: Raja resigns as Telecom Minister. Kapil Sibal has given additional charge of Telecom Ministry

November 10, 2010: CAG submits report on 2G spectrum to government stating loss to Rs 1.76 lakh crore to exchequer

September 27, 2010: ED informs the apex court of the probe against companies.

Oct 21, 2009: CBI files FIR against unknown officers of DoT, unknown private persons/companies and others.

Jan 10, 2008: DoT decides to issue licences on the first-come-first-serve basis (FCFS), preponing cut-off date to September 25.

Nov 22, 2007: Finance Ministry writes to DoT raising concerns over the procedure adopted.

Nov 2, 2007: Then Prime Minister Manmohan Singh writes to Raja to ensure fair licence allocation and proper revision of entry fee.

Oct 1, 2007: DoT receives 575 applications from 46 firms.

Sept 25, 2007: Telecom Ministry issues press note fixing a deadline for application as of October 1, 2007.

Aug 2007: Process of allotment of the 2G spectrum along with Unified Access Services (UAS) Licences initiated by the Department of Telecom (DoT).

May 2017: A Raja takes over as telecom minister

The ED had listed 19 accused in its charge sheet in April 2014, including Raja, Kanimozhi, Shahid Balwa, Vinod Goenka, Asif Balwa, Rajiv Aggarwal, Karim Morani, P Amirtham and Sharad Kumar in connection with the case. Besides Kalaignar TV, the other companies accused in the case are STPL (now Etisalat DB Telecom (P) Ltd), Kusegaon Realty (P) Ltd, Cineyug Media & Entertainment (P) Ltd, Dynamix Realty, Eversmile Construction Company (P) Ltd, Conwood Construction & Developers (P) Ltd, DB Realty Ltd and Mystical Construction (P) Ltd (earlier known as Nihar Constructions (P).

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2G spectrum: Centre moves SC for modification of 2012 verdict, here is why

More than 12 years after it was delivered, the Centre on Monday moved the Supreme Court seeking modification of its verdict in the 2G spectrum case which said the State was duty-bound to adopt the auction route while transferring or alienating the country's natural resources.

In its judgement delivered on February 2, 2012, the apex court had quashed 2G spectrum licences given to various firms during the tenure of A Raja as the telecom minister in January 2008.

On Monday, Attorney General R Venkataramani, appearing for the Centre, mentioned an interim application before a bench of Chief Justice D Y Chandrachud and Justice J B Pardiwala.

While seeking urgent listing of the application, the top law officer told the bench that the plea seeks modification of the 2012 verdict as the Centre wanted to grant 2G spectrum licences in some cases.

Advocate Prashant Bhushan, who appeared for NGO Centre for Public Interest Litigation which was one of the petitioners on whose plea the February 2012 verdict was delivered, opposed the application and said the issue has been well-settled by the apex court in its judgement that auction is the only mode for granting licences for natural resources like spectrum, the radio frequencies allocated to the mobile phone industry for communication over the airwaves.

"We will see, you please move an e-mail," the CJI told Venkataramani.

In its 2012 judgement, the apex court had said, "When it comes to alienation of scarce natural resources like spectrum etc, it is the burden of the State to ensure that a non-discriminatory method is adopted for distribution and alienation, which would necessarily result in protection of national/public interest”.

The top court had said in its view, a duly publicised auction conducted fairly and impartially was perhaps the best method for discharging this burden. "In other words, while transferring or alienating the natural resources, the State is duty bound to adopt the method of auction by giving wide publicity so that all eligible persons can participate in the process," it had said.

On March 22 this year, the Delhi High Court had admitted a CBI appeal against the acquittal of Raja and 16 others in the 2G spectrum allocation case, paving the way for hearing the matter six years after the plea was filed by the agency.

Admitting the Central Bureau of Investigation's appeal, the high court had said there were "some contradictions" in the trial court's judgment which require "deeper examination".

A special court had on December 21, 2017, acquitted Raja, DMK MP Kanimozhi and others in the CBI and ED cases related to the 2G spectrum allocation. On March 20, 2018, the CBI had approached the high court, challenging the special court's judgment.

The CBI had alleged there was a loss of Rs 30,984 crore to the exchequer in allocation of licences for 2G spectrum which were scrapped by the top court on February 2, 2012.

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Full text of judgments in 2G spectrum scam cases

A special today has acquitted 19 accused including former telecom minister a raja and dmk leader kanimozhi in cases relating to the 2g spectrum scam cases. here are judgments of the three cases..

Former telecom minister A Raja, Rajya Sabha MP Kanimozhi and several others were cleared on Thursday of their alleged role in the 2G spectrum allocation scam.

A special today has acquitted 19 accused including former telecom minister A Raja and DMK leader Kanimozhi in 2G spectrum scam cases.(HT Photos)

“I have absolutely no hesitation in holding that (the) prosecution has miserably failed to prove any charge against any accused,” special court judge OP Saini told a packed courtroom.

The 2G spectrum scam came to light when the Comptroller and Auditor General concluded that the tender process for 2G spectrum allocation was tainted by bribes and wholesale fraud.

In 2012, the Supreme Court cancelled 122 licences for eight firms.

Here are the court’s judgments in the three cases related to the 2G spectrum scam case:

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2G: The key players – a virtual who’s who from the worlds of politics, business, bureaucracy and law

For those who came in late, a quick roundup..

On Thursday, a special court acquitted all the accused in the 2G spectrum allocation case. They include politicians, bureaucrats and corporate executives who had been charged with undervaluing the telecom spectrum so as to favour select companies, thereby defrauding the public exchequer.

Alleged to be the largest corruption scandal in India, the 2G case shook up the country’s political arrangement. It was used by the Bharatiya Janata Party and its affiliates in the Sangh Parivar to paint the Congress-led United Progressive Alliance government of the time as incorrigibly corrupt, and win power in 2014.

Here is a list of the key players in the case, what role they played and where they stand today.

Politicians

Andimuthu Raja

Then : A Raja, minister for communications and information technology between May 2007 and November 2010, was the main accused in the case. The four-time Dravida Munnetra Kazhagam MP was arrested by the Central Bureau of Investigation in 2011 after the Comptroller and Auditor General of India alleged he had indulged in corruption. In February 2012, the Supreme Court held that Raja “wanted to favour some companies at the cost of the Public Exchequer” thereby cancelling 122 licences awarded by the Union government.

Now : In May 2012, Raja was granted bail on the condition that he stay away from his home state of Tamil Nadu and the office of telecom department. He was politically inactive since, but is now expected to return to mainstream Tamil politics. After his acquittal, Raja thanked his party chief M Karunanidhi, writing , “I place the 2G verdict at your feet with gratitude. You preserved me in snow so I wouldn’t dissolve in the spectrum battle.”

MK Kanimozhi

Then : Kanimozhi is the daughter of DMK chief Karunanidhi and his third wife Rajathi Ammal. She was one of the owners of Kalaignar TV, which allegedly received bribes from a telecom company on behalf of Raja. In court, the CBI called Kanimozhi the “controlling mind behind Kalaignar TV”. She was arrested in May 2011 and spent nearly six months in jail before being bailed out .

Now : Like Raja, the acquittal is certain to resurrect Kanimozhi’s political career. She is currently a Rajya Sabha MP representing Tamil Nadu.

Legal eagles

Subramanian Swamy

Then : Much before he merged his Janata Party with the Bharatiya Janata Party, Subramanian Swamy played a key role in the 2G case. He wrote letters to then Prime Minister Singh and filed a criminal complaint against Raja and others in the Supreme Court. Swamy had also alleged that then Union home minister P Chidambaram should be made a co-accused with Raja, as the latter could not have moved ahead without a nod from the finance ministry, headed by Chidambaram at the time of spectrum allotment.

Now : Swamy was mostly ignored by the Modi government when it took office in 2014 despite his key role in raking muck in the 2G case. In 2016, he was nominated (not elected) to the Rajya Sabha by the Union government. The acquittals in the 2G case have come as a blow to Swamy, and he has attacked the Modi government. “My party is not serious when it comes to dealing with corruption,” said Swamy, arguing that there might be repercussion in 2019, and blamed it on internal

Prashant Bhushan

Then: Acting on behalf of the Centre for Public Interest Litigation, lawyer and activist Prashant Bhushan was the first to move the Delhi High Court seeking cancellation of 2G licences issued in 2008 on 2001 prices, alleging that rules were bent to favour the rich and the influential, when a competitive bidding process should have been followed. Eventually the Supreme Court not only cancelled the licences but also ordered a court-monitored investigation by the Central Bureau of Investigation. Bhushan had also sought the appointment of a Special Investigation Team to oversee the CBI’s inquiry, which was turned down.

Now: The activist-lawyer has also turned to politics since then. After being briefly associated with the Aam Aadmi Party, he is now is a founding member of the Swaraj Abhiyan. “I don’t know on what basis the court said there was no evidence,” Bhushan said . “In at least 40 pages of the judgment, the court has reproduced the evidence given by the CBI showing illegal gratification.”

Goolam Essaji Vahanvati

Then : Rai was the Comptroller and Auditor General of India at the time Manmohan Singh was the prime minister. Rai calculated that the alleged scam in the allocation of 2G spectrum was worth Rs 1.76 lakh crore, which if true, would have made it one of the largest corruption scandals anywhere in the word, ever.

Now : In January 2017, Rai was appointed interim president of the Board of Control for Cricket in India, one of the richest sports bodies in the world. He was also chosen to head the powerful Banks Board Bureau by the Modi government in 2016. The Bureau will oversee improvement in the governance of public sector banks and has the power to recommend the heads of various institutions.

Siddharth Behura

Then : Behura was secretary of the Department of Telecommunication from January 1, 2008 to September 30, 2009. According to the CBI, Behura conspired with Raja to favour certain telecom companies in acquiring spectrum licences.

Now : Behuria was arrested by the CBI in 2011 but granted bail in just a little over a year.

RK Chandolia

Then : Chandolia was Raja’s private secretary when the licences were granted. As per the CBI’s chargesheet in the case, Chandolia also conspired with Raja to favour certain telecom companies. Along with Behura, Chandolia allegedly shut application counters to block competing companies from applying for licences.

Now : Arrested by the CBI in 2011, he was granted bail in 2012. He was acquitted of all charges on Thursday.

Ranjit Sinha

Then : Sinha led the CBI from 2012 to 2014 when it was investigating the 2G case. He came under a cloud after a residential entry register was discovered that showed some of the accused in the case had been frequently visiting him. The Supreme Court even asked him to remove himself from the case.

Now : Sinha says he feels vindicated given that a special court has found no evidence of corruption. “Internal communications had been leaked and there was a vilification campaign against me,” Sinha said . “My reputation was in tatters and with this trial court judgment, I feel vindicated today.”

Business executives

Sanjay Chandra

Then : Chandra, former Unitech Wireless managing director and chairman of the board of Uninor, was accused of collaborating with Raja to acquire spectrum at an unjustifiably low price. Soon after acquiring the licences, Unitech entered into a joint venture with Telenor, a Norwegian state-owned telecommunications company.

Now : Following the cancellation of the 2G licences by the Supreme Court, the joint venture was dissolved , with Unitech exiting and all Uninor assets being transferred to Telenor. Uninor was the player most hit by the cancellation of licences given it had 36.3 million customers and licences in 22 circles. Chandra was acquitted of all charges by the special court on Thursday.

Vinod Goenka

Then : Goenka was the managing director of Swan Telecom, a company floated by DB Realty, and was accused of cheating and conspiracy in the allocation of 2G spectrum. Moreover, Swan Telecom was accused of being a front company for the Reliance Anil Dhirubhai Ambani Group. As a shell company, Swan would have been ineligible to even bid for the spectrum, much less acquire it. DB Realty, a major real estate company, was promoted by Goenka and his partner Shahid Balwa.

Now : After all the accused were acquitted on Thursday, the stock of DB Realty surged .

Ravi Ruia and Anshuman Ruia

Then : The Ruias were charged with criminal conspiracy and cheating under the Indian Penal Code. They were accused of using Look Telecom as a front to secure 2G licences.

Now : The Ruias are promoters of the Essar Group, a large conglomorate with interests in energy, metals, mining, infrastructure, among other sectors.

Reliance Anil Dhirubhai Ambani Group executives

Then : The CBI charge sheet of April 2011 included the names of then group managing director Gautam Doshi, and senior vice presidents Hari Nair and Surendra Pipara. The CBI alleged that Reliance Telecom Ltd, the telecom division of Reliance ADAG, had used Swan Telecom Pvt Ltd. as a front company to get the 2G licences, funding it through a “circuitous route”. Group chairman Anil Ambani was granted the status of a prosecution witness on the understanding that he could assist in the prosecution case against his own executives. The CBI special prosecutor, UU Lalit – who is now a Supreme Court judge – was reported to have declared Ambani a “hostile witness”, which was denied by Ambani’s lawyer who said Ambani was suffering from a “ memory loss ”.

Now: Reliance ADAG group says it welcoms the verdict and acquittal of its three executives. The group has recently been in news for its defence arm, Reliance Defence Limited, signing a joint venture for the biggest-ever Indian defence deal with Dassault Aviation for the manufacture of Rafale aircraft.

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CURRENT AFFAIRS
VERDICT IN THE 2G SPECTRUM CASE

MONTH/YEARWISE ARCHIVES

Verdict in the 2G Spectrum Case

Why in news?

A CBI court recently acquitted all the accused in the 2G spectrum allocation scam case.

How did it all begin?

In September 2007, the Department of Telecom (DoT) issued just a week’s time for companies to apply for mobile phone licences.
As spectrums were priced artificially low, a mad scramble followed and 575 applications were received, most of which were from little known firms.
The DoT then issued 122 licences by adopting a controversial ‘first-come first-served policy’, which privileged those who applied at the earliest.
A CAG report in 2008 on 2G spectrum allocations, estimated a loss of R1.76 lakh crore to the exchequer.
Consequently, in 2010, Mr. Raja resigned as telecom minister and he was later arrested in early 2011.
Notably, the Delhi High Court set up a special court to fast-track the case.

How did the case proceed?

CBI filed its chargesheet and subsequently DMK MP ‘Ms. Kanimozhi’ and the MD of “Kalaignar TV” ‘Mr. Sharad Kumar’ were also arrested in late 2011.
CBI also filed an FIR against another DMK leader and former telecom minister Dayanidhi Maran and his brother kalanithi Maran.
Overall, the trial began against 17 people, that included the telecom executives of Unitech, Swan Telecom and Reliance Anil Dhirubhai Ambani Group.
In early 2012, Supreme Court cancelled all the 122 telecom licences allocated to nine companies in 2007, by holding ‘first-come, first-served’ policy at fault.
Income-Tax department, in 2013, submitted to the SC, the recordings of 5,800 tapped controversial phone conversations between corporate lobbyist Niira Radia and politicians.
Enforcement Directorate (ED), in its 2014 chargesheet, accused Mr. Raja, and Ms. Kanimozhi of money laundering.
In 2015, CBI records in court that the Mr. Raja “misled” the then PM Manmohan Singh on policy matters pertaining to 2G spectrum allocation.
Finally, the special court concluded its hearing in April 2017, and it recently pronounced its final order, which aquitted all the people.
It remains to be seen if the case is proceeded ahead with appeals against the current order in higher courts (HC and SC).

What are the policy spin-offs from the case?

SC’s order that cancelled all the 122 2G licences issued in 2008 was perceieved as a judicial over-reach into the policy domain.
Hence, it moved a presidential reference with eight questions, that included the rationale of “auction being the only mode for allocation of resources”.
On hearing the presidential reference, by five-judge constitution bench, the SC concluded uphoplding the primacy of the government in the policy domain.
It also explicitly stated that auctions is not a must for all resource allocations and that maximisation of revenue cannot be the sole criterion in all situations.

Source: Hindustan Times

Decision on E-way bills

Prelims practice questions 21-12-2017.

2G Spectrum Case: A List Of Accused Who Walked Free Today

Former telecom minister a raja and dmk leader kanimozhi, and 15 others walked free from a delhi court today after they were acquitted of charges in the "2g spectrum scam", which has been dubbed as 'india's biggest telecom scandal'..

2G Spectrum Scam Case: A Raja, Kanimozhi and 15 others acquitted today.

Here's the list of all 17 who were accused in the 2G Spectrum case:

1. Kanimozhi Karunanidhi, DMK leader and party chief M Karunanidhi's daughter 2. Former telecom minister A Raja 3. Former telecom secretary Siddharth Behura 4. A Raja's former private secretary RK Chandolia

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2G Spectrum Case: Centre Moves Supreme Court Seeking Modification Of 2012 Verdict

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2G SPECTRUM SCAM

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2G spectrum case: Delhi High Court admits CBI’s plea challenging acquittal of former telecom minister A Raja, others

A special cbi court had in december 2017 acquitted a raja, dmk supremo karunanidhi’s daughter kanimozhi, and other accused of all charges in the 2g spectrum cases..

The Delhi High Court on Friday admitted the CBI’s appeal challenging the acquittal of former telecom minister A Raja and others in the 2G spectrum allocation case, observing that the allegation is not about an “ordinary criminal offence” but an “economic offence” which is a separate class and is “required to be handled with a different approach”.

The court passed the judgment on the Central Bureau of Investigation’s (CBI) “leave to appeal”, paving the way for dealing with the appeal on merits against the trial court’s verdict. The high court thereafter listed the matter on May 20. A leave to appeal is a formal permission granted to appeal against the decision of a court.

In December 2017, a special CBI judge had acquitted Raja, Dravida Munnetra Kazhagam (DMK) supremo Karunanidhi ’s daughter Kanimozhi, and other accused of all charges in the 2G spectrum allocation cases. The CBI had moved the high court in this regard in 2018.

A single-judge bench of Justice Dinesh Kumar Sharma on Friday said, “In such cases it is not necessary that actually someone has benefitted or not. The case of some of the accused persons cannot be segregated at this stage as the facts are so much interwoven with each other that it will be difficult to separate at this stage. It is also pertinent to mention that evidence oral in nature cannot be discarded outrightly merely because it is not corroborated by any documentary evidence.”

“The evidence has to be weighed and not counted. The court during the hearing has also noticed some contradictions in the judgment itself, which requires deeper examination. The court at this stage is required to have a prima facie helicopter view. There may be a possibility that such contradictions are explained by the defence during the hearing,” the judge added.

Justice Sharma in the 120-page order, also observed that the trial judge had “repeatedly noted” that the prosecution should have given an opportunity to the witness to explain the statement made by them. The high court said that this gives rise to the concern “why the Ld. Judge presiding over the trial did not exercise his jurisdiction” under the Indian Evidence Act to seek any clarity, if there was any ambiguity.

The judge noted that the court must apply its judicial mind before granting or refusing leave to appeal. It said that it is “fully conscious of the fact that the presumption of innocence which exists in favour of the accused at the beginning of the trial strengthens with the order of acquittal in its favour”.

It said that it is also fully conscious that leave cannot be granted “merely because” the high court considers that an alternative view could have been taken by it. It, however, also observed that this was a case where the “allegations were extremely serious in nature” which had to be handled in a very different manner.

“The court on the basis of material on record, and after going through the sworn testimonies, material on record, impugned judgement and the submissions made at bar by both the parties has reached on an objective satisfaction that there is a prima facie case which requires deeper examination and re-appreciation/re-appraisal of entire evidence. In view of the discussion made herein above, arguable points have been made out by the CBI thereby converting grant of Leave to Appeal,” Justice Sharma said.

Raja, Kanimozhi, and the other accused were acquitted of all charges in the 2G spectrum allocation cases — famously known as the 2G “scam” — by a special CBI judge on December 21, 2017.

The special court, which had held that there was no scam in the 2G spectrum allocation in 2007-08 when Raja was the telecom minister in the erstwhile United Progressive Alliance (UPA) regime headed by Manmohan Singh , also set free all the accused in two offshoot cases lodged by the Enforcement Directorate (ED) and the CBI.

Previously, Justice Brijesh Sethi of the high court heard the matter and released the case on November 23, 2020, in view of his retirement on November 30, 2020, stating that the pleas would be listed before a new bench.

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Open access
Published: 19 April 2024

GbyE: an integrated tool for genome widely association study and genome selection based on genetic by environmental interaction

Xinrui Liu 1 , 2 ,
Mingxiu Wang 1 ,
Jie Qin 1 ,
Yaxin Liu 1 ,
Shikai Wang 1 ,
Shiyu Wu 1 ,
Ming Zhang 1 ,
Jincheng Zhong 1 &
Jiabo Wang 1

BMC Genomics volume 25 , Article number: 386 ( 2024 ) Cite this article

131 Accesses

Metrics details

The growth and development of organism were dependent on the effect of genetic, environment, and their interaction. In recent decades, lots of candidate additive genetic markers and genes had been detected by using genome-widely association study (GWAS). However, restricted to computing power and practical tool, the interactive effect of markers and genes were not revealed clearly. And utilization of these interactive markers is difficult in the breeding and prediction, such as genome selection (GS).

Through the Power-FDR curve, the GbyE algorithm can detect more significant genetic loci at different levels of genetic correlation and heritability, especially at low heritability levels. The additive effect of GbyE exhibits high significance on certain chromosomes, while the interactive effect detects more significant sites on other chromosomes, which were not detected in the first two parts. In prediction accuracy testing, in most cases of heritability and genetic correlation, the majority of prediction accuracy of GbyE is significantly higher than that of the mean method, regardless of whether the rrBLUP model or BGLR model is used for statistics. The GbyE algorithm improves the prediction accuracy of the three Bayesian models BRR, BayesA, and BayesLASSO using information from genetic by environmental interaction (G × E) and increases the prediction accuracy by 9.4%, 9.1%, and 11%, respectively, relative to the Mean value method. The GbyE algorithm is significantly superior to the mean method in the absence of a single environment, regardless of the combination of heritability and genetic correlation, especially in the case of high genetic correlation and heritability.

Conclusions

Therefore, this study constructed a new genotype design model program (GbyE) for GWAS and GS using Kronecker product. which was able to clearly estimate the additive and interactive effects separately. The results showed that GbyE can provide higher statistical power for the GWAS and more prediction accuracy of the GS models. In addition, GbyE gives varying degrees of improvement of prediction accuracy in three Bayesian models (BRR, BayesA, and BayesCpi). Whatever the phenotype were missed in the single environment or multiple environments, the GbyE also makes better prediction for inference population set. This study helps us understand the interactive relationship between genomic and environment in the complex traits. The GbyE source code is available at the GitHub website ( https://github.com/liu-xinrui/GbyE ).

Peer Review reports

Genetic by environmental interaction (G × E) is crucial of explaining individual traits and has gained increasing attention in research. It refers to the influence of genetic factors on susceptibility to environmental factors. In-depth study of G × E contributes to a deeper understanding of the relationship between individual growth, living environment and phenotypes. Genetic factors play a role in most human diseases at the molecular or cellular level, but environmental factors also contribute significantly. Researchers aim to uncover the mechanisms behind complex diseases and quantitative traits by investigating the interactions between organisms and their environment. Common, complex, or rare human diseases are often considered as outcomes resulting from the interplay of genes, environmental factors, and their interactions. Analyzing the joint effects of genes and the environment can provide valuable insights into the underlying pathway mechanisms of diseases. For instance, researchers have successfully identified potential loci associated with asthma risk through G × E interactions [ 1 ], and have explored predisposing factors for challenging-to-treat diseases like cancer [ 2 , 3 ], rhinitis [ 4 ], and depression [ 5 ].

However, two main methods are currently being used by breeders in agricultural production to increase crop yields and livestock productivity [ 6 ]. The first is to develop varieties with relatively low G × E effect to ensure stable production performance in different environments. The second is to use information from different environments to improve the statistical power of genome-wide association study (GWAS) to reveal potential loci of complex traits. The first method requires long-term commitment, while the second method clearly has faster returns. In GWAS, the use of multiple environments or phenotypes for association studies has become increasingly important. This not only improves the statistical power of environmental susceptibility traits[ 7 ], but also allows to detect signaling loci for G × E. There are significant challenges when using multiple environments or phenotypes for GWAS, mainly because most diseases and quantitative traits have numerous associated loci with minimal impact [ 8 ], and thus it is impossible to determine the effect size regulated by environment in these loci. The current detection strategy for G × E is based on complex statistical model, often requiring the use of a large number of samples to detect important signals [ 9 , 10 ]. In GS, breeders can use whole genome marker data to identify and select target strains in the early stages of animal and plant production [ 11 , 12 , 13 ]. Initially, GS models, similar to GWAS models, could only analyze a single environment or phenotype [ 14 ]. To improve the predictive accuracy of the models, higher marker densities are often required, allowing the proportion of genetic variation explained by these markers to be increased, indirectly obtaining higher predictive accuracy. It is worth mentioning that the consideration of G × E and multiple phenotypes in GS models [ 15 ] has been widely studied in different plant and animal breeding [ 16 ]. GS models that allow G × E have been developed [ 17 ] and most of them have modeled and interpreted G × E using structured covariates [ 18 ]. In these studies, most of the GS models provided more predictive accuracy when combined with G × E compared to single environment (or phenotype) analysis. Hence, there is need to develop models that leverage G × E information for GWAS and GS studies.

This study developed a novel genotype-by-environment method based on R, termed GbyE, which leverages the interaction among multiple environments or phenotypes to enhance the association study and prediction performance of environmental susceptibility traits. The method enables the identification of mutation sites that exhibit G × E interactions in specific environments. To evaluate the performance of the method, simulation experiments were conducted using a dataset comprising 282 corn samples. Importantly, this method can be seamlessly integrated into any GWAS and GS analysis.

Materials and methods

Support packages.

The development purpose of GbyE is to apply it to GWAS and GS research, therefore it uses the genome association and prediction integrated tool (GAPIT) [ 19 ], Bayesian Generalized Linear Regression (BGLR) [ 20 ], and Ridge Regression Best Linear Unbiased Prediction (rrBLUP) [ 21 ]package as support packages, where GbyE only provides conversion of interactive formats and file generation. In order to simplify the operation of the GbyE function package, the basic calculation package is attached to this package to support the operation of GbyE, including four function packages GbyE.Simulation.R (Dual environment phenotype simulation based on heritability, genetic correlation, and QTL quantity), GbyE.Calculate.R (For numerical genotype and phenotype data, this package can be used to process interactive genotype files of GbyE), GbyE.Power.FDR.R (Calculate the statistical power and false discovery rate (FDR) of GWAS), and GbyE.Comparison.Pvalue.R (GbyE generates redundant calculations in GWAS calculations, and SNP effect loci with minimal p -values can be filtered by this package).

Samples and sequencing data

In this study, a small volume of data was used for software simulation analysis, which is widely used in testing tasks of software such as GAPIT, TASSEL, and rMPV. The demonstration data comes from 282 inbred lines of maize, including 4 phenotypic data. In any case, there are no missing phenotypes in these data, and this dataset can be obtained from the website of GAPIT ( https://zzlab.net/GAPIT/index.html , accessed on May 1, 2022). Among them, our phenotype data was simulated using a self-made R simulation function, and the Mean and GbyE phenotype files were calculated. Convert this format to HapMap format using PLINK v1.09 and scripts written by oneself.

Simulated traits

Phenotype simulation was performed by modifying the GAPIT.Phenotype.Simulation function in the GAPIT. Based on the input parameter NQTN, the random selected markers’ genotype from whole genome were used to simulate genetic effect in the simulated trait. The genotype effects of these selected QTNs were randomly sampled from a multivariate normal distribution, the correlation value between these normal distribution was used to define the genetic relationship between each environments. The additive heritability ( ${{\text{h}}}_{{\text{g}}}^{2}$ ) was used to scale the relationship between additive genetic variance and phenotype variance. The simulated phenotype conditions in this paper are set as follows: 1) The three levels of ${{\text{h}}}_{{\text{g}}}^{2}$ were set at 0.8, 0.5, and 0.2, representing high ( ${{\text{h}}}_{{\text{h}}}^{2}$ ), median ( ${{\text{h}}}_{{\text{m}}}^{2}$ ) and low ( ${{\text{h}}}_{{\text{l}}}^{2}$ ) heritability; 2) Genetic correlation were set three levels 0.8, 0.5, 0.2 representing high ( ${{\text{R}}}_{{\text{h}}}$ ), medium ( ${{\text{R}}}_{{\text{m}}}$ ) and low ( ${{\text{R}}}_{{\text{l}}}$ ) genetic correlation; 3) 20 pre-set effect loci of QTL. The phenotype values in each environment were simulated together following above parameters.

Genetic by environment interaction model

The pipeline analysis process of GbyE includes three steps: data preprocessing, production converted, Association analysis. Normalize the phenotype data matrix Y of the dual environment and perform GbyE conversion to generate phenotype data in GbyE.Y format. The genotype data format, such as hapmap, vcf, bed and other formats firstly need to be converted into numerical genotype format (homozygotes were coded as 0 or 2, heterozygotes were coded as 1) using software or scripts such as GAPIT, PLINK, etc. The environment (E) matrix is environment index matrix. The G (n × m) originally of genotype matrix was converted as GbyE.GD(2n × 2 m) $\left[\begin{array}{cc}G& 0\\ G& G\end{array}\right]$ during the Kronecker product, and the Y vector (n × 1) was also converted as the GbyE.Y vector (2n × 1) after normalization. The duplicated data format indicated different environments, genetic effect, and populations. The genomic data we used in the analysis was still retained the whole genome information. The first column of E is the additive effect, which was the average genetic effect among environments. The others columns of E are the interactive effect, which should be less one column than the number of environments. Because it need to avoid the linear dependent in the model. In the GbyE algorithm, we coded the first environment as background as default, that means the genotype in the first environment are 0, the others are 1. Then the Kronecker product of G and environment index matrix was named as GbyE.GD. The interactive effect part of the GbyE.GD matrix in the GWAS and GS were the relative values based on the first environment (Fig. 1 ). The GbyE environmental interaction matrix can be easily obtained by constructing the interaction matrix E (e.g., Eq. 1 ) such that the genotype matrix G is Kronecker-product with the design interaction matrix E (e.g., Eq. 2 ), in which $\left[\begin{array}{c}G\\ G\end{array}\right]$ matrix is defined as additive effect and $\left[\begin{array}{c}0\\ G\end{array}\right]$ matrix is defined as interactive effect. $\left[\begin{array}{cc}G& 0\\ G& G\end{array}\right]$ matrix is called gene by environment interaction matrix, hereinafter referred to as the GbyE matrix. The phenotype file (GbyE.Y) and genotype file (GbyE.GD) after transformation by GbyE will be inputted into the GWAS and GS models and computed as standard phenotype and genotype files.

where G is the matrix of whole genotype and E is the design matrix for exploring interactive effects. GbyE mainly uses the Kronecker product of the genetic matrix (G) and the environmental matrix (E) as the genotype for subsequent GWAS as a way to distinguish between additive and interactive effects.

The workflow pipeline of GbyE. The GbyE contains three main steps. (Step 1) Preprocessing of phenotype and genotype data,. The phenotype values in each environment was normalized respectively. Meanwhile, all genotype from HapMap, VCF, BED, and other types were converted to numeric genotype; (Step 2) Generate GbyE phenotype and interactive genotype matrix through the transformation of GbyE. In GbyE.GD matrix, the blue characters indicate additive effect, and red ones indicate interactive effect; (Step 3) The MLM and rrBLUP and BGLR were used to perform GWAS and GS

Association analysis model

The mixed linear model (MLM) of GAPIT is used as the basic model for GWAS analysis, and the principal component analysis (PCA) parameter is set to 3. Then the p -values of detection results are sorted and their power and FDR values are calculated. General expression of MLM (Fig. 1 ):

where Y is the vector of phenotypic measures (2n × 1); PCA and SNP i were defined as fixed effects, with a size of (2n × 2 m); Z is the incidence matrix of random effects; μ is the random effect vector, which follows the normal distribution μ ~ N(0, ${\delta }_{G}^{2}$ K) with mean vector of 0 and variance covariance matrix of ${\delta }_{G}^{2}$ K, where the ${\delta }_{G}^{2}$ is the total genetic variance including additive variance and interactive variance, the K is the kinship matrix built with all genotype including additive genotype and interactive genotype; e is a random error vector, and its elements need not be independent and identically distributed, e ~ N(0, ${\delta }_{e}^{2}$ I), where the ${\delta }_{e}^{2}$ is the residual and environment variance, the I is the design matrix.

Detectivity of GWAS

In the GWAS results, the list of markers following the order of P-values was used to evaluate detectivity of GWAS methods. When all simulated QTNs were detected, the power of the GWAS method was considered as 1 (100%). From the list of markers, following increasing of the criterion of real QTN, the power values will be increasing. The FDR indicates the rate between the wrong criterion of real QTNs and the number of all un-QTNs. The mean of 100 cycles was used to consider as the reference value for statistical power comparison. Here, we used a commonly used method in GWAS research with multiple traits or environmental phenotypes as a comparison[ 22 ]. This method obtains the mean of phenotypic values under different conditions as the phenotypic values for GWAS analysis, called the Mean value method, Compare the calculation results of GbyE with the additive and interactive effects of the mean method to evaluate the detection power of the GbyE strategy. Through the comprehensive analysis of these evaluation indicators, we aim to comprehensively evaluate the statistical power of the GbyE strategy in GWAS and provide a reference for future optimization research.

Among them, the formulae for calculating Power and FDR are as follows:

where ${{\text{n}}}_{{\text{i}}}$ indicates whether the i-th detection is true, true is 1, false is 0; ${{\text{m}}}_{{\text{r}}}$ is the total number of all true QTLs in the sample size; the maximum value of Power is 1.

where ${{\text{N}}}_{{\text{i}}}$ represents the i-th true value detected in the pseudogene, true is 1, false is 0. and cumulative calculation; ${{\text{M}}}_{{\text{f}}}$ is the number of all labeled un-QTNs in the total samples; the maximum value of FDR is 1.

Genomic prediction

To comparison the prediction accuracy of different GS models using GbyE, we performed rrBLUP, Bayesian methods using R packages. All phenotype of reference population and genotype of all population were used to train the model and predict genomic estimated breeding value (gEBV) of all individuals. The correlation between real phenotypes and gEBV of inference population was considered as prediction accuracy. fivefold cross-validation and 100 times repeats was performed to avoid over prediction and reduce bias. In order to distinguish the additive and interactive effects in GbyE, we designed two lists of additive and interactive effects in the "ETA" of BGLR, and put the additive and interactive effects into the model as two kinships for random objects. However, it was not possible to load the gene effects of the two lists in rrBLUP, so the additive and interactive genotypes together were used to calculate whole genetic kinship in rrBLUP (Fig. 1 ). Relevant parameters in BGLR are set as follows: 1) model set to "RRB"; 2) nIter is set to "12000"; 3) burnIn is set to "10000". The results of the above operations are averaged over 100 cycles. We also validated the GbyE method using four other Bayesian methods (BayesA, BayesB, BayesCpi, and Bayesian LASSO) in addition to RRB in BGLR.

Partial missing phentoype in the prediction

In this study, we artificially missed phenotype values in the single and double environments in the whole population from 281 inbred maize datasets. In the missing single environment case, the inference set in the cross-validation was selected from whole population, and each individual in the inference were only missed phenotypes in the one environment. The phenotype in the other environment was kept. The genotypes were always kept. In the case of missing double environments, both phenotypes and genotypes of environment 1 and environment 2 are missing, and the model can only predict phenotypic values in the two missing environments through the effects of other markers. In addition, the data were standardized and unstandardized to assess whether standardization had an effect on the estimation of the model. This experiment was tested using the "ML" method in rrBLUP to ensure the efficiency of the model.

GWAS statistical power of models at different heritabilities and genetic correlations

Power-FDR plots were used to demonstrate the detection efficiency of GbyE at three genetic correlation and three genetic power levels, with a total of nine different scenarios simulated (from left to right for high and low genetic correlation and from top to bottom for high and low genetic power). In order to distinguish whether the effect of improving the detection ability of genome-wide association analysis in GbyE is an additive effect or an effect of environmental interactions, we plotted their Power-FDR curves separately and added the traditional Mean method for comparative analysis. As shown in Fig. 2 , GbyE algorithm can detect more statistically significant genetic loci with lower FDR under any genetic background. However, in the combination with low heritability (Fig. 2 A, B, C), the interactive effect detected more real loci than GbyE under low FDR, but with the continued increase of FDR, GbyE detected more real loci than other groups. Under the combination with high heritability, all groups have high statistical power at low FDR, but with the increase of FDR, the statistical effect of GbyE gradually highlights. From the analysis of heritability combinations at all levels, the effect of heritability on interactive effect is not obvious, but GbyE always maintains the highest statistical power. The average detection power of GWAS in GbyE can be increased by about 20%, and with the decrease of genetic correlation, the effect of GbyE gradually highlights, indicating that the G × E plays a role.

The power-FDR testing in simulated traits. Comparing the efficacy of the GbyE algorithm with the conventional mean method in terms of detection power and FDR. From left to right, the three levels of genetic correlation are indicated in order of low, medium and high. From top to bottom, the three levels of heritability, low, medium and high, are indicated in order. (1) Inter: Interactive section extracted from GbyE; (2) AddE: Additive section extracted from GbyE; (3) ${{\text{h}}}_{{\text{l}}}^{2}$ , ${{\text{h}}}_{{\text{m}}}^{2}$ , ${{\text{h}}}_{{\text{g}}}^{2}$ : Low, medium, high heritability; (4) ${{\text{R}}}_{{\text{l}}}$ , ${{\text{R}}}_{{\text{m}}}$ , ${{\text{R}}}_{{\text{l}}}$ : where R stands for genetic correlation, represents three levels of low, medium and high

Resolution of additive and interactive effect

The output results of GbyE could be understood as resolution of additive and interactive genetic effect. Hence, we created a combined Manhattan plots with Mean result from MLM, additive, and interactive results from GbyE. As shown in Fig. 3 , true marker loci were detected on chromosomes 1, 6 and 9 in Mean, and the same loci were detected on chromosomes 1 and 6 for the additive result in GbyE (the common loci detected jointly by the two results were marked as solid gray lines in the figure). All known pseudo QTNs were labeled with gray dots in the circle. Total 20 pseudo QTNs were simulated in such trait (The heritability is set to 0.9, and the genetic correlation is set to 0.1). Although the additive section in GbyE did not catch the locus on chromosome 9 yet (those p-values of markers did not show above the significance threshold (p-value < 3.23 × 10 –6 )), it has shown high significance relative to other markers of the same chromosome. In the reciprocal effect of GbyE, we detected more significant loci on chromosomes 1, 2, 3 and 10, and these loci were not detected in either of the two previous sections. An integrate QQ plot (Fig. 3 D) shows that the overall statistical power of the additive section in Mean and GbyE are close, nevertheless, the interactive section in the GbyE provided a bit of inflation.

Manhattan statistical comparison plot. Manhattan comparison plots of mean ( A ), additive ( B ) and gene-environment interactive sections ( C ) at a heritability of 0.9 and genetic correlation of 0.1. Different colors are used in the diagram to distinguish between different chromosomes (X-axis). Loci with reinforcing circles and centroids are set up as real QTN loci. Consecutive loci found in both parts are shown as id lines, and loci found separately in the reciprocal effect only are shown as dashed lines. Parallel horizontal lines indicate significance thresholds ( p -value < 3.23 × 10 –6 ). D Quantile–quantile plots of simulated phenotypes for demo data from genome-wide association studies. x-axis indicates expected values of log p -values and y-axis is observed values of log p -values. The diagonal coefficients in red are 1. GbyE-inter is the interactive section in GbyE; GbyE-AddE is the additive section in GbyE

Genomic selection in assumption codistribution

The prediction accuracy of GbyE was significantly higher than the Mean value method by model statistics of rrBLUP in most cases of heritability and genetic correlation (Fig. 4 ). The prediction accuracy of the additive effect was close to that of Mean value method, which was consistent with the situation under the low hereditary. The prediction accuracy of interactive sections in GbyE remains at the same level as in GbyE, and interactive section plays an important role in the model. We observed that in ${{\text{h}}}_{{\text{l}}}^{2}{{\text{R}}}_{{\text{h}}}$ (Fig. 4 C), ${{\text{h}}}_{{\text{m}}}^{2}{{\text{R}}}_{{\text{h}}}$ (Fig. 4 F), ${{\text{h}}}_{{\text{h}}}^{2}{{\text{R}}}_{{\text{l}}}$ (Fig. 4 G), the prediction accuracy of GbyE was slightly higher than the Mean value method, but there was no significant difference overall. In addition, we only observed that the prediction accuracy of GbyE was slightly lower than the Mean value method in ${{\text{h}}}_{{\text{h}}}^{2}{{\text{R}}}_{{\text{l}}}$ (Fig. 4 H), but there was still no significant difference between GbyE and Mean value methods. Under the combination of low heritability and genetic correlation, the prediction accuracy of Mean value method and additive effect model remained at a similar level. However, with the continuous increase of heritability and genetic correlation, the difference in prediction accuracy between the two gradually increases. In summary, the GbyE algorithm can improve the accuracy of GS by capturing information on multiple environment or trait effects under the rrBLUP model.

Box-plot of model prediction accuracy. The prediction accuracy (pearson's correlation coefficient) of the GbyE algorithm was compared with the tradition al Mean value method in a simulation experiment of genomic selection under the rrBLUP operating environment. The effect of different levels of heritability and genetic correlation on the prediction accuracy of genomic selection was simulated in this experiment. Each row from top to bottom represents low heritability ( ${{\text{h}}}_{{\text{l}}}^{2}$ ), medium heritability ( ${{\text{h}}}_{{\text{m}}}^{2}$ ) and high heritability ( ${{\text{h}}}_{{\text{h}}}^{2}$ ), respectively; each column from left to right represents low genetic correlation ( ${{\text{R}}}_{{\text{l}}}$ ), medium genetic correlation ( ${{\text{R}}}_{{\text{m}}}$ ) and high genetic correlation ( ${{\text{R}}}_{{\text{h}}}$ ), respectively; The X-axis shows the different test methods and effects, and the Y-axis shows the prediction accuracy

Genomic selection in assumption un-codistribution

The overall performance of GbyE under the 'BRR' statistical model based on the BGLR package remained consistent with rrBLUP, maintaining high predictive accuracy in most cases of heritability and genetic relatedness (Fig. S1 ). However, when the heritability is set to low and medium, the difference between the prediction accuracy of GbyE algorithm and Mean value method gradually decreases with the continuous increase of genetic correlation, and there is no statistically significant difference between the two. The prediction accuracy of the model by GbyE in ${{\text{h}}}_{{\text{h}}}^{2}{{\text{R}}}_{{\text{l}}}$ (Fig. S1 G) and ${{\text{h}}}_{{\text{h}}}^{2}{{\text{R}}}_{{\text{h}}}$ (Fig. S1 I) is significantly higher than that by Mean value method when the heritability is set to be high. On the contrary, when the genetic correlation is set to medium, there is no significant difference between GbyE and Mean value method in improving the prediction accuracy of the model, and the overall mean of GbyE is lower than Mean. When GbyE has relatively high heritability and low genetic correlation, its prediction accuracy is significantly higher than the mean method, such as ${{\text{h}}}_{{\text{m}}}^{2}{{\text{R}}}_{{\text{l}}}$ (Fig. S1 D), ${{\text{h}}}_{{\text{h}}}^{2}{{\text{R}}}_{{\text{l}}}$ (Fig. S1 G), and ${{\text{h}}}_{{\text{h}}}^{2}{{\text{R}}}_{{\text{m}}}$ (Fig. S1 H). Therefore, GbyE is more suitable for situations with high heritability and low genetic correlation.

Adaptability of Bayesian models

Next, we tested a more complex Bayesian model. The GbyE algorithm and Mean value method were combined with five Bayesian algorithms in BGLR for GS analysis, and the computing R script was used for phenotypic simulation test, where heritability and genetic correlation were both set to 0.5. The results indicate that among the three Bayesian models of RRB, BayesA, and BayesLASSO, the predictive accuracy of GbyE is significantly higher than that of Mean value method (Fig. 5 ). In contrast, under the Bayesian models of BayesB and BayesCpi, the prediction accuracy of GbyE is lower than that of the Mean value method. The GbyE algorithm improves the prediction accuracy of the three Bayesian models BRR, BayesA, and BayesLASSO using information from G × E and increases the prediction accuracy by 9.4%, 9.1%, and 11%, respectively, relative to the Mean value method. However, the predictive accuracy of the BayesB model decreased by 11.3%, while the BayescCpi model decreased by 6%.

Relative prediction accuracy histogram for different Bayesian models. The X-axis is the Bayesian approach based on BGLR, and the Y-axis is the relative prediction accuracy. Where we normalize the prediction accuracy of Mean (the prediction accuracy is all adjusted to 1); the prediction accuracy of GbyE is the increase or decrease value relative to Mean in the same group of models

Impact of all and partial environmental missing

We tested missing the environmental by using simulated data. In the case of the simulated data, we simulated a total of nine situations with different heritability and genetic correlations (Fig. 6 ) and conducted tests on single and dual environment missing. The improvement in prediction accuracy by the GbyE algorithm was found to be significantly higher than the Mean value method in single environment deletion, regardless of the combination of heritability and genetic correlation. In the case of ${{\text{h}}}_{{\text{h}}}^{2}{{\text{R}}}_{{\text{h}}}$ , the prediction accuracy of GbyE is higher than 0.5, which is the highest value among all simulated combinations. When GbyE estimates the phenotypic values of Environment 1 and Environment 2 separately, its predictive accuracy seems too accurate. On the other hand, when the phenotypic values of both environments are missing on the same genotype, the predictive accuracy of GbyE does not show a significant decrease, and even maintains accuracy comparable to that of a single environment missing. However, when GbyE estimates Environment 1 and Environment 2 separately, the prediction accuracy significantly decreases compared to when a single environment is missing, and the prediction accuracy of Environment 1 and Environment 2 in ${{\text{h}}}_{{\text{l}}}^{2}{{\text{R}}}_{{\text{m}}}$ is extremely low (Fig. 6 B). In addition, the prediction accuracy of GbyE is lower than Mean values only in ${{\text{h}}}_{{\text{l}}}^{2}{{\text{R}}}_{{\text{h}}}$ , whether it is missing in a single or dual environment.

Prediction accuracy of simulated data in single and dual environment absence. The prediction effect of GbyE was divided into two parts, environment 1 and environment 2, to compare the prediction accuracy of GbyE when predicting these two parts separately. This includes simulations with missing phenotypes and genotypes in environment 1 only ( A ) and simulations with missing in both environments ( B ). The horizontal coordinates of the graph indicate the different combinations of heritabilities and genetic correlations of the simulations

The phenotype of organisms is usually controlled by multiple factors, mainly genetic [ 23 ] and environmental factors [ 24 ], and their interactive factors. The phenotype of quantitative traits is often influenced by these three factors [ 25 , 26 ]. However, based on the computing limitation and lack of special tool, the interactive effect always was ignored in most GWAS and GS research, and it is difficult to distinguish additive and interactive effects. The rate between all additive genetic variance and phenotype variance was named as narrow sense heritability. The accuracy square of prediction of additive GS model is considered that can not surpass narrow sense heritability. In this study, the additive effects in GbyE are essentially equivalent to the detectability of traditional models, the key advantage of GbyE is the interactive section. More significant markers with interactive effects were detected. Detecting two genetic effects (additive and interactive sections) in GWAS and GS is a boost to computational complexity, while obtaining genotypes for genetic interactions by Kronecker product is an efficient means. This allows the estimation of additive and interactive genetic effects separately during the analysis, and ultimately the estimated genetic effects for each GbyE genotype (including additive and interactive genetic effect markers) are placed in a t-distribution for p -value calculation, and the significance of each genotype is considered by multiple testing. The GbyE also expanded the estimated heritability as generalized heritability which could be explained as the rate between total genetics variance and phenotype variance.

The genetic correlation among traits in multiple environments is the major immanent cause of GbyE. When the genetic correlation level is high, then additive genetic effects will play primary impact in the total genetic effect, and interactive genetic effects with different traits or environments are often at lower levels [ 27 ]. Therefore, the statistical power of the GbyE algorithm did not improve significantly compared with the traditional method (Mean value) when simulating high levels of genetic correlation. On the contrary, in the case of low levels of genetic correlation, the genetic variance of additive effects is relatively low and the genetic variance of interactive effects is major. At this time, GbyE utilizes multiple environments or traits to highlight the statistical power. Since the GbyE algorithm obtains additive, environmental, and interactive information by encoding numerical genotypes, it only increases the volume of SNP data and can be applied to any traditional GWAS association statistical model. However, this may slightly increase the correlation operation time of the GWAS model, but compared to other multi environment or trait models [ 28 , 29 ], GbyE only needs to perform a complete traditional GWAS once to obtain the results.

In GS, rrBLUP algorithm is a linear mixed model-based prediction method that assumes all markers provide genetic effects and their values following a normal distribution [ 30 ]. In contrast, the BGLR model is a linear mixed model, which assumes that gene effects are randomly drawn from a multivariate normal distribution and genotype effects are randomly drawn from a multivariate Gaussian process, which takes into account potential pleiotropy and polygenic effects and allows inferring the effects of single gene while estimating genomic values [ 31 ]. The algorithm typically uses Markov Chain Monte Carlo methods for estimation of the ratio between genetic variances and residual variances [ 32 , 33 ]. The model has been able to take into account more biological features and complexity, and therefore the overall improvement of the GbyE algorithm under BGLR is smaller than Mean method. In addition, the length of the Markov chain set on the BGLR package is often above 20,000 to obtain stable parameters and to undergo longer iterations to make the chain stable [ 34 ]. GbyE is effective in improving the statistical power of the model under most Bayesian statistical models. In the case of the phenotypes we simulated, more iterations cannot be provided for the BayesB and BayesCpi models because of the limitation of computation time, which causes low prediction accuracy. It is worth noting that the prediction accuracy of BayesCpi may also be influenced by the number of QTLs [ 35 ], and the prediction accuracy of BayesB is often related to the distribution of different allele frequencies (from rare to common variants) at random loci [ 36 ].

The overall statistical power of GbyE was significantly higher in missing single environment than in missing double environment, because in the case of missing single environment, GbyE can take full advantage of the information from the phenotype in the second environment. And the correlation between two environments can also affect the detectability of the GbyE algorithm in different ways. On the one hand, a high correlation between two environments can improve the predictive accuracy of the GbyE algorithm by using the information from one environment to predict the breeding values in the other environment, even if there is only few relationship with that environment [ 37 , 38 ]. On the other hand, when two environments are extremely uncorrelated, GbyE algorithm trained in one environment may not export well to another environment, which may lead to a decrease in prediction accuracy [ 39 ]. In the testing, we found that when the GbyE algorithm uses a GS model trained in one environment and tested in another environment, the high correlation between environments may result to the model capturing similarities between environments unrelated to G × E information [ 40 ]. However, when estimating the breeding values for each environment separately, GbyE still made effective predictions using the genotypes in that environment and maintained high prediction accuracy. As expected, the additive effect calculates the average genetic effect between environments, and its predictive effect does not differ much from the mean method. The interactive effect, however, has one less column than the number of environments, and it calculates the relative values between environments, a component that has a direct impact on the predictive effect. The correlation between the two environments may have both positive and negative effects on the detectability of the GbyE, so it is important to carefully consider the relationship between the two environments in subsequent in development and testing.

A key advantage of the GbyE algorithm is that it can be applied to almost all current genome-wide association and prediction. However, the focus of GbyE is still on estimating additive and interactive effects separately, so that it is easy to determine which portion of the is playing a role in the computational estimation.. The GbyE algorithm may have implications for the design of future GS studies. For example, the model could be used to identify the best environments or traits to include in GS studies in order to maximize prediction accuracy. It is particularly important to test the model on large datasets with different genetic backgrounds and environmental conditions to ensure that it can accurately predict genome-wide effects in a variety of contexts.

GbyE can simulate the effects of gene-environment interactions by building genotype files for multiple environments or multiple traits, normalizing the effects of multiple environments and multiple traits on marker effects. It also enables higher statistical power and prediction accuracy for GWAS and GS. The additive and interactive effects of genes under genetic roles could be revealed clearly, which makes it possible to utilize environmental information to improve the statistical power and prediction accuracy of traditional models, thus helping us to better understand the interactions between genes and the environment.

Availability of data and materials

The GbyE source code, demo script, and demo data are freely available on the GitHub website ( https://github.com/liu-xinrui/GbyE ).

Abbreviations

Genome-widely association study

Genome selection

Genetic by environmental interaction

Genome association and prediction integrated tool

Mixed linear model

Bayesian generalized linear regression

Ridge regression best linear unbiased prediction

False discovery rate

Principal component analysis

Genomic estimated breeding value

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Acknowledgements

Thank you to all colleagues in the laboratory for their continuous help.

This project was partially funded by the National Key Research and Development Project of China, China (2022YFD1601601), the Heilongjiang Province Key Research and Development Project, China (2022ZX02B09), the Qinghai Science and Technology Program, China (2022-NK-110), Sichuan Science and Technology Program, China (Award #s 2021YJ0269 and 2021YJ0266), the Program of Chinese National Beef Cattle and Yak Industrial Technology System, China (Award #s CARS-37), and Fundamental Research Funds for the Central Universities, China (Southwest Minzu University, Award #s ZYN2023097).

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Xinrui Liu, Mingxiu Wang, Jie Qin, Yaxin Liu, Shikai Wang, Shiyu Wu, Ming Zhang, Jincheng Zhong & Jiabo Wang

Nanchong Academy of Agricultural Sciences, Nanchong, 637000, China

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JW and XL conceived and designed the project. XL managed the entire trial, conducted software code development, software testing, and visualization. MW, JQ, YL, SW, MZ and SW helped with data collection and analysis. JQ, and YL assisted with laboratory analyses. JW, and XL had primary responsibility for the content in the final manuscript. JZ supervised the research. JW designed software and project methodology. All authors approved the final manuscript. All authors have reviewed the manuscript.

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Liu, X., Wang, M., Qin, J. et al. GbyE: an integrated tool for genome widely association study and genome selection based on genetic by environmental interaction. BMC Genomics 25 , 386 (2024). https://doi.org/10.1186/s12864-024-10310-5

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GbyE: an integrated tool for genome widely association study and genome selection based on genetic by environmental interaction

Conclusions

Materials and methods

Samples and sequencing data

Simulated traits

Genetic by environment interaction model

Association analysis model

Detectivity of GWAS

Genomic prediction

Partial missing phentoype in the prediction

GWAS statistical power of models at different heritabilities and genetic correlations

Resolution of additive and interactive effect

Genomic selection in assumption codistribution

Genomic selection in assumption un-codistribution

Adaptability of Bayesian models

Impact of all and partial environmental missing

Availability of data and materials

Abbreviations

Acknowledgements

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